Manifold SVM analog circuit fault diagnosis method based on particle swarm optimization

Abstract

The invention discloses a manifold SVM analog circuit fault diagnosis method based on particle swarm optimization. The method comprises the main steps: software is used to simulate faults of a diagnosis object; for each fault in a circuit, Monte Carlo analysis is used to detect a feature signal of the fault, a wavelet packet is used to decompose the fault signal, signal decomposition has the maximum law based on the optimal wavelet entropy principle, and an optimal energy value of each group of signals is extracted to serve as a feature value of the fault; and during fault classification, a particle swarm algorithm is used, parameter optimization is carried out on a weight parameter and a penalty parameter in support vectors considering sample data class intervals, and thus, the optimal hyperplane of an SVM has better classification effects, and the fault diagnosis accuracy is improved.

Description

technical field [0001] The invention belongs to the technical field of analog circuits, and is a manifold SVM analog circuit fault diagnosis method based on particle swarm optimization. The improved particle swarm optimization algorithm diagnoses the faults of the SVM (Support Vector Machine, Support Vector Machine) simulation circuit with optimized parameters. Background technique [0002] The advent of the era of big data has led to the rapid development of industrial intelligence, and analog circuits have also appeared in various fields, and the diagnosis of circuit failures has become particularly important. However, complex and diverse analog circuits, continuous fault parameters, tolerances of components and other factors limit the development of its diagnostic technology. The popularity of the concept of artificial intelligence has promoted the development of machine learning algorithms, and more scholars have applied it to the fault diagnosis of analog circuits. Xi...

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Problems solved by technology

Xin Jian and others proposed a method combining wavelet transform and neural network, but the neural network itself has shortcomings, such as many training samples, over-learning, and poor robustness.

He Yongjun, Zeng Wenying and others proposed the information entropy support vector machine algorithm to diagnose the fault of the sensor circuit. Although it improves the generalization ability and shortens the diagnosis time, it ignores the internal structural characteristics of the data and cannot obtain accurate diagnosis information. , affecting the classification performance of SVM

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